12 years in government taught me something surprising about data science.

Making money and making an impact aren’t always the same thing.

The easiest way to create value as a data scientist is to help your organisation to make more money.

After all, everyone wants more money, don’t they? As Elon Musk’s recent $1 trillion pay deal suggests, even the richest person on Earth.

Yet, while money is valuable, money and value aren’t necessarily the same thing.

And if you work for a not-for-profit or government organisation, or in a large organisation where your ability to impact the bottom-line is minimal, then you might need to look somewhere else.

One thing I learned while working in government for 12 years was that, while cost reduction initiatives were always welcome, if a project proposed a way to:

• Help stakeholders make better decisions; or
• Help make my stakeholders’ lives easier in some way.

Then it would almost certainly be approved and highly valued.

Looking back, the data projects that I was able to gain the most traction with, during my time in those roles, consistently fit into one of those two categories.

For example:

• The AI automation project that reduced the workloads of a data monitoring team allowing them to go from working overtime every weekend to going home on time every night - and paved the way for other similar AI initiatives; and
• The improved decision-making model that reduced the number of community complaints on an issue from several per month to zero per year.

Both of these opportunities emerged after I finally learned to listen to the complaints and pains of those around me.

Treat your stakeholders’ frustrations as signposts pointing to where your data science skills can make the biggest difference.

Your data science work doesn’t need to create money, but it does need to create value.

Figure out what your stakeholders really value and focus on delivering that.

Talk again soon,

Dr Genevieve Hayes